Association of Faculty Perceptions of Work–Life With Emotional Exhaustion and Intent to Leave Academic Nursing: Report on a National Survey of Nurse Faculty

Data Collection

A national survey of a random, stratified sample representative of all full-time faculty was conducted from October 2010 to May 2011. A two-stage sampling strategy was designed to yield a minimum of 3,000 survey respondents. First, a comprehensive list was compiled of schools and programs that offered, at a minimum, one prelicensure degree program. The list was assembled using the most recent roster of prelicensure programs published by the National Council of State Boards of Nursing and supplemented by information compiled by the Association of American Colleges of Nursing and the directories of the National League for Nursing Accrediting Commission and the Commission on Collegiate Nursing Education. From this list, a sample was selected that was proportionally representative of all programs in the United States by prelicensure degree (ADN or BSN) offered, urbanization (urban or rural), and research intensity (defined as residing in a doctoral-granting institution or not, adopted from the Carnegie Classification System [Carnegie Foundation for the Advancement of Teaching, 2009]). Information for stratifying the sample was gathered from the database of the National Center for Education Statistics and the Carnegie Classification dataset. Using standard survey procedures, the sample was drawn in replicates (i.e., smaller subsets, each of which was representative and stratified as noted previously) to monitor responses to ensure a yield of 3,000 faculty members.

Second, rosters were compiled of full-time faculty at these randomly selected schools, and the accuracy of each school’s roster was confirmed with the respective dean’s office. All full-time faculty members (n = 3,975) teaching in undergraduate or graduate programs or both at the selected schools were invited to participate. Questionnaires, administered by mail and e- mail, with telephone follow-up, were completed by 3,120 faculty members, for a response rate of 78.5% from 269 schools. Prior to data collection, the research was approved by the Institutional Review Board at Rutgers University. Only respondents with no missing data on variables in the regression models were included in the analyses.

Measures

Emotional Exhaustion. To measure emotional exhaustion, the emotional exhaustion (EE) subscale from the Maslach Burnout Inventory (MBI) was used, which is a standard measure of occupational burnout that has been extensively validated in multiple disciplines (Maslach, Jackson, & Leiter, 1996). Of the three subscales composing the MBI (the other two are depersonalization and personal accomplishment), the EE scale has been established theoretically and empirically as measuring the core element of burnout (Kallaith, O’Driscoll, Gillespie, & Bluedorn, 2000 ; Koeske & Koeske, 1993; Lee & Ashforth, 1993 ; Leiter, 1993; Reilly, 1994). Maslach (2003) described emotional exhaustion as emotional overload that is characterized by feelings of being drained and used up by one’s work and lacking energy to face another day. To reduce respondent burden, three of the nine items composing the EE subscale were used. Principal component analyses were conducted of responses to all nine items in an existing dataset, consisting exclusively of nurse faculty, which were collected but not analyzed for a broader study of RNs (Flynn, Liang, Dickson, & Aiken, 2010 ). Consistent with Maslach’s theory, the three items representing feelings of being drained, feelings of being used up, and lack of energy were identified as those that best predict the overall nine-item score (ranging from 0 to 54, using a Likert-type, 7-point response scale in which 0 = never and 6 = every day). The aim was to approximate the MBI-EE subscale to apply Maslach’s criteria for differentiating faculty members reporting high levels of emotional exhaustion from those reporting average or low levels. That threshold (>27 on a score of 0 to 54 among workers in the health professions) has been demonstrated by numerous studies to identify individuals who are at risk for adverse health consequences and diminished capacity to perform their jobs (Cimiotti et al., 2012; Cropanzano et al., 2003; Hakanen & Schaufeli, 2012; McHugh, Kutney-Lee, Cimiotti, Sloane, & Aiken, 2011; Wright & Cropanzano, 1998).

To approximate the Maslach MBI-EE scale using the three items, an adjustment ratio was calculated by dividing the mean of the nine items by the mean of the three items, which was then applied to the data; 38.8% of faculty members were classified as reporting high levels of emotional exhaustion, according to Maslach’s criteria. To provide validation of this adjustment strategy, a larger existing data set was used of more than 14,000 RNs working in varied settings who were randomly sampled and surveyed in a study of the effects of work environments on performance outcomes (Flynn et al., 2010). The adjustment ratio was then applied to estimate the average total score; the estimated score was 19.6, compared with the actual score of 19.4, for a difference of 1%. In addition, in calculating an adjustment ratio based on the validation sample and applying it to the data, 38.8% of faculty members were classified as having high emotional exhaustion, which is identical to the percentage designated by using the ratio calculated with the current study data.

Intent to Leave Academic Nursing. Faculty members were asked how likely they were to leave teaching in the next 5 years, using a 4-point response scale (1 = very likely, 2 = somewhat likely, 3 = somewhat unlikely, 4 = very unlikely). For the dependent variable, the responses were dichotomized (somewhat or very likely to leave, contrasted with somewhat or very unlikely).

Domains of Predictors. A comprehensive set of factors was incorporated to predict emotional exhaustion and intent to leave academic nursing in the current study models. The following variables were selected to capture spheres of influence having varying levels of specificity to the respondent’s role as a nurse faculty member:

1. Demographic and background factors that are generally established prior to entry into the profession, including age, gender, race/ethnicity, and highest nursing degree as currently reported (78% of respondents earned their highest nursing degree prior to accepting a full-time faculty position).

2. Characteristics of the respondent’s institution that affect all of those employed there (whether they teach at a school offering an ADN or BSN degree, research intensity of the school, and urban/rural locale).

3. Characteristics of the respondent’s particular job and status (rank, tenure status, whether he or she is teaching in a postbaccalaureate program, extent of administrative responsibilities, salary).

4. Their reported performance in their role, including workload (number of hours per week devoted to all work activities, service on school or departmental committees) and productivity (development of new courses, revising courses, conversion to online format, number of articles publications, preparation of grant applications).

5. Perceptions of the adequacy of their work and role (dissatisfaction with aspects of work–life).

6. Health status, which may be affected by factors from all levels.

Within each domain, those items that were significantly related to the outcome variables in bivariate analyses (p < 0.05) were retained in the analysis. In a few cases, the variables were included for conceptual completeness, whether or not they met the bivariate criterion (e.g., the institutional characteristics that served as stratifying variables in the sampling).

Suited to the focus of the analysis, particular attention was devoted to the fifth domain (i.e., perceptions of the adequacy of their work and role). Items were developed that reflect the terms and conditions of work (e.g., salary, workload, job security, availability of resources), as well as the content of work (e.g., meaningfulness, autonomy and independence, rewards for innovation). Respondents rated satisfaction with each (very satisfied, somewhat satisfied, somewhat dissatisfied, very dis-satisfied). None of the items retained in the analysis were inter-correlated at a level of 0.60 or higher. Table 1 and the Figure provide details on all predictors.

Table 1: Characteristics of Study Sample (N = 2,401) and Reported Aspects of Work–Life

Figure. Dissatisfaction with aspects of work–life among nurse faculty (N = 2,401; N reflects all responses included in either or both of the regression models presented in Tables 3 and 4.)

Analysis

To identify factors associated with emotional exhaustion and intent to leave academic nursing, logistic regression models were estimated for each of these dependent variables using Stata statistical software. Stata svyset and svy commands were used to adjust for complex design effects (i.e., clustering due to the two- stage sampling procedure and stratification).

Predictors of Emotional Exhaustion

As shown in Table 3, major significant predictors of emotional exhaustion were dissatisfaction with workload (odds ratio [OR] = 1.82), devoting 50% or more time to administrative responsibilities (OR = 1.73), dissatisfaction with flexibility to balance work and family life (OR = 1.58), reporting fair or poor health status (OR = 1.46), dissatisfaction with the meaningfulness of work (OR = 1.36) and with the direction of the department or school (OR = 1.27), and time spent on all work activities (OR = 1.02). For example, an increase in total workload from 40 to 50 hours per week was associated with 22% higher odds of emotional exhaustion.

Table 3: Predictors of High Level of Emotional Exhaustion: Logistic Regression Model (N = 2,400)

To assess the proportion of variance in emotional exhaustion explained by the current study’s model (R²) and the relative contribution of various facets of work–life, a linear regression model was also estimated, with a continuous version of the emotional exhaustion score as the dependent variable (calculated as the total score based on the 0 to 6 response to each item of the scale). Predictors were entered in six blocks (Table A; available in the online version of this article). The content of the six blocks is the same as in Table 3; however, the order of entry of the blocks reflects the level of specificity of the domains of variables to the faculty member’s particular role (as described more fully previously) —individual characteristics, institution characteristics, job characteristics, reported workload and productivity, dissatisfaction with aspects of work–life, and health status. The model accounted for 40% of the variation in emotional exhaustion (R² = 0.40). Upon entry into the model, dissatisfaction with elements of work–life accounted for 28% of the variation in emotional exhaustion, 7% was explained by reported workload and productivity, 2% was accounted for by job characteristics, 2% by health status, and 1% by other individual characteristics; institution characteristics did not contribute to emotional exhaustion in the estimated model.

Predictors of Intent to Leave Academic Nursing

Several factors significantly predicted faculty members’ intent to leave academic nursing within the next 5 years (Table 4)—age within 5 years of retirement (OR = 6.41), having a BSN (OR = 2.70) or MSN ( OR = 1.91) as the highest nursing degree earned, not holding tenure in an institution that offers tenure (OR = 1.49); years full-time on a faculty (each additional year was associated with a 6% added likelihood of leaving); not working on an advanced nursing degree (OR = 1.59); being certified as an advanced practice nurse (OR = 1.56); reporting high emotional exhaustion (OR = 1.27); and dissatisfaction with availability of teaching support (OR = 1.19), workload (OR = 1.26), salary (OR = 1.18) and rewards for innovation on the job (OR = 1.16). The model was estimated, removing respondents who were within 5 years of retirement, and the results were essentially the same, except that assistant professors were slightly less likely to intend to leave.

Table 4: Predictors of Intent to Leave Academic Nursing in 5 Years: Logistic Regression Model (N = 2,226)

To assess the proportion of variance explained, a linear regression model was estimated with intent to leave as a 4-point ordinal measure (not shown) and an order of entry, reflecting the same conceptualization as the model of emotional exhaustion. In this model, 11.5% of the variation in intent to leave was accounted for by age, and another 14% was explained by other domains in the model; chief among them were dissatisfaction with aspects of work–life (8.4%), job characteristics and status (4%), and reported workload and productivity (1%).

Predictors of Emotional Exhaustion and Intent to Leave Academic Nursing

Although overall job satisfaction was high, those faculty members who registered dissatisfaction with specific elements of work–life were more likely to report high emotional exhaustion and an intent to leave academic nursing within 5 years. Most important among work–life predictors of emotional exhaustion were dissatisfaction with workload and flexibility to balance work and family life, followed by negative assessments of the meaningfulness of work and the direction of the department or school. With respect to intent to leave academic nursing, the contribution of aspects of work–life was approximately equal to that of proximity to retirement age, and material concerns played a prominent role; dissatisfaction with workload, salary, and availability of teaching support were significant predictors, as was emotional exhaustion. Interestingly, those nonmaterial dimensions of work–life that garnered the highest rates of dissatisfaction—the direction of the department or school and relationship with the school administration—were not significant predictors of intent to leave academic nursing. It is conceivable that such nonmaterial dimensions may be associated with intent to leave the current employer, rather than leave the field, which is a prospect that merits further research attention. Although job turnover may not directly affect the numbers of faculty members in the field, the associated disruption may affect teaching productivity.

The extent to which perceptions of burden accounted for emotional exhaustion, as well as intent to leave, is particularly worrisome. Recommendations of recent high-profile commissions, such as the Institute of Medicine’s reports on the future of nursing (Institute of Medicine, 2010) and the needs of older Americans ( Institute of Medicine, 2008) as well as provisions of the federal health reform legislation (Patient Protection and Affordable Care Act, 2010) call for educating more nurses and preparing them to play a broader spectrum of roles in our health care delivery system (e.g., establishing nurse-managed community health centers, providing primary care to newly insured populations, leading interprofessional collaborations). Preparing the nurse workforce with the expertise for assuming these roles will intensify the pressures on existing faculty capacity, adding to those very burdens that contribute to the current nurse faculty shortage and undermine commitment to teach. A clear need exists to address faculty workload and working conditions and to monitor faculty perspectives on those elements of work–life that are associated with emotional exhaustion and intent to leave as identified in this article. A Web interface to the data of the predictors analyzed in the current study is publicly available for individual nursing programs to compare their own faculty members with national averages. The questionnaire can be downloaded to collect similar data from faculty in individual programs. The data interface from the current study can be customized for programs of similar scope, research intensity, and other key characteristics to facilitate meaningful comparisons ( http://www.evaluatinginnovationsinnursing.org/nufaqs-nurse-faculty-data-query/?intro=Yes).

The finding that faculty members with advanced practice nurse certification (APRNs) were 1.5 times more likely to express an intention to leave teaching is disconcerting. The projected increase in demand for primary care services associated with implementation of the Affordable Care Act has been accompanied by growing recognition that APRNs can make a significant contribution to meeting that need. Without faculty members with APRN certification, the capacity to prepare APRNs will be severely constrained. More knowledge of the particular needs of this subgroup of faculty may be helpful to the efforts of recruiting and retaining them.

Strategies for Change

Although strategies to increase teaching capacity are widely considered to address the faculty shortage (Joynt & Kimball, 2008), these approaches may inadvertently contribute to high rates of faculty exhaustion and may exacerbate faculty shortages. Efforts to enhance faculty capacity must take into account the likely impact on faculty workload and work–life; yet, most of these strategies have lacked systematic evaluation (Wyte-Lake, Tran, Bowman, Needleman, & Dobalian, 2013). Acknowledging the importance of generating sound evidence of the effectiveness of such approaches in addressing the faculty shortage, the Robert Wood Johnson Foundation established a national program, Evaluating Innovations in Nursing Education, to support studies of the impact of these strategies on teaching productivity, faculty work–life, and the quality of nursing education. Evaluation researchers in collaboration with nursing programs around the country are completing controlled evaluations of innovations focused on increasing faculty teaching productivity and efficiency. These initiatives, described below, offer promise in addressing teaching capacity, without aggravating those elements of work–life associated with the negative outcomes studied in this work.

• Collaborations among educational programs and delivery organizations designed to better align health system resources with the clinical education needs of students (dedicated education units [DEUs]). DEUs may reduce the stress on academic faculty of supervising care of patients in delivery settings in which they have little or no standing.
• Reliance on clinical simulation to make more effective use of instructional expertise and technology to improve student learning. Extensive use of clinical simulation may reduce the workload of academic faculty by delegating clinical skills evaluation to staff specialists who use clinical scenarios and highfidelity technology to assess students’ clinical competencies.
• Development of a single baccalaureate curriculum among a regional or statewide consortium of nursing education programs to make more efficient use of faculty resources and accelerate student progression. Participation in an academic consortium facilitates sharing of faculty to reduce course loads and also promotes collaboration with colleagues in developing shared course materials, thus potentially reducing preparation time for individual faculty.

Other projects supported by Evaluating Innovations in Nursing Education address strategies for increasing the supply of doctorally prepared nurses and aim to increase our understanding of students’ decision making about pursuing doctoral education and faculty careers. These include:

• Implementation of policies offering incentives for graduate-prepared nurses to pursue faculty careers. These support-for-service programs may contribute to lowering faculty vacancy rates and commensurate decreases in faculty course load.
• Analyses of factors influencing career choice among current doctoral students in nursing, stages of decision making leading to a career in nursing education, and prospects of early-entry doctoral programs for increasing the capacity of nurse faculty.

Findings from these studies, which are intended to inform strategies for increasing the numbers of new faculty members, will be available in the coming months (Robert Wood Johnson Foundation, 2014).